Travel times and distance in your SERPs

Answering short travel questions on the results page.

You can see Ana’s thoughts on the topic over here on her post titled “Latest Changes in Google Search Results”.

The kinds of information Google displays on their results seem to get more interesting and complicated. Recently an SEO specialist I work with, Ana Diaz, saw something interesting. A number of travel queries came with a map, with a route, distance and travel time displayed. Like the special dates details seen last month, there was not much either on Google’s blogs or elsewhere online about this.

While it seems that adding this information to the main search results is a new thing from Google, displaying this kind of information isn’t much of a departure for the search engine. Google Now, available on Android, provides the same kind of information automatically based on your location and time of day. Google Maps has also provided this kind of information for a few years now, and has even been providing their traffic data via API.

Google Now's travel information

Google Now’s travel information

Understanding the Query

A less than semantic search

Unusually easily confused for Google.

Unfortunately, as cool as this new kind of result is, it is easily confused. The map with travel details does not display consistently across a number of queries. While it will show for “St Lucia to Newstead” and “St Lucia from Newstead” (two Brisbane suburbs) it won’t for “Drive from St Lucia to Newstead” or “Travel to St Lucia from Newstead”. It won’t support more than two destinations in a sequence either. For now, it appears to only be able to return a result for “location” “direction” “location”. It will also accept other qualifying location terms like “St Lucia to Newstead Brisbane” or “St Lucia from Newstead Australia”. Just like the fields available in Google Maps.

A close match in syntax

As in Google Maps, so it is in Google search, for now.

It is interesting how closely the queries used in Google’s main search need to match to the format seen in Google Maps. It seems to indicate that this feature is not as closely integrated with Google’s search as you would expect.

As cool as it is, there are a number of ways this tool seems to fail. A search for “Ascot to Manly” won’t return a map, which isn’t surprising as a number of cities have suburbs with these names. Adding a location qualifier like “Ascot to Manly Brisbane” doesn’t help, nor does the fact that Google is pretty certain I am in Brisbane, QLD. However it will work with “Surry Hills to Paddington”, although there are suburbs called Paddington in both Sydney and Brisbane.

Paddington Sydney or Paddington Brisbane?

Paddington Sydney or Paddington Brisbane?

How good is it really?

Not so vague in Google Maps

Not so vague in Google Maps

Travel time and distance results in Google’s main search results are interesting, and they do appear to be new. When they work, they are useful, and it does not appear to be using any information that they have not had for a while, or used in the same way elsewhere. It is interesting how sensitive to query structure this feature is, especially given how good Google usually is with poorly structured and spelled searches. Even Google Maps seems to be able to cope with some of the searches that stymied this other feature.

Facebook lets you Google Social

Getting search in your social

Facebook is finally bringing social search. Or more accurately, adding search to social. Facebook has actually been providing a social layer to search for a while now, with both Bing and Blekko using Facebook information to help curate their results for signed in users. Facebook’s Graph Search is different. From the information available to date, it seems to be far more about adding search to their users’ social experience.

The initial rollout will be very limited, with Facebook saying that it will only cover the following areas at first:

People: “friends who live in my city,” “people from my hometown who like hiking,” “friends of friends who have been to Yosemite National Park,” “software engineers who live in San Francisco and like skiing,” “people who like things I like,” “people who like tennis and live nearby”

Photos: “photos I like,” “photos of my family,” “photos of my friends before 1999,” “photos of my friends taken in New York,” “photos of the Eiffel Tower”

Places: “restaurants in San Francisco,” “cities visited by my family,” “Indian restaurants liked by my friends from India,” “tourist attractions in Italy visited by my friends,” “restaurants in New York liked by chefs,” “countries my friends have visited”

Interests: “music my friends like,” “movies liked by people who like movies I like,” “languages my friends speak,” “strategy games played by friends of my friends,” “movies liked by people who are film directors,” “books read by CEOs”

As it is only in limited beta, there isn’t much discussion about its ability to handle natural language queries or what kind of information is available. But there is one other interesting thing to come from today’s announcement: Bing’s involvement.

Bing and Facebook

On their own blog shortly after the announcement from Facebook, Bing outlined how they were involved in “Evolving Search on Facebook”. Bing has worked with Facebook since 2008, starting by powering their web search, with AdCentre placing ads next to the organic results. Since then, their relationship appears to have worked, with Bing continuing to provide search while Facebook took over the ads in 2010 and also hinted at bringing public data from Facebook’s API into their own search experience in October 2009. Far more recently Facebook data has been integrated into Bing’s search results.

Putting the Value into the Search in Social

If all the rage surrounding Facebook’s attempts to find the perfect way to handle EdgeRank is to be believed, they have a real discoverability problem. The news feed works just fine for some light social stalking, and with their existing search features all but broken, it is almost impossible to unearth the kind of information that Facebook seems to want to target with Graph Search, and somehow advertise against.

Graph Search should fix discoverability and provide more navigation tools to their users, allowing Facebook to turn one of the world’s larger collections of user information into something both engaging and useful. Or at least as engaging and useful as the quality of the information it has collected would allow.

Seen from a data perspective, the acquisition of Instagram and Facebook’s integration services such as Spotify make a lot of sense. While Facebook is far more device-agnostic than it used to be, it still does not completely own its users’ online life. Providing a platform and social integration for services such as Spotify and outright buying others like Instagram extends Facebook’s ability to collect information on its users’ behaviour beyond its own touch points.

Why this Matters?

Ultimately Facebook needs impressions. Facebook’s main source of revenue is advertising, from banners to sponsored stories. These ads lose value and more importantly traffic when there is no-one there to look at them. To ensure that there are enough eyeballs to go around for the advertising inventory that Facebook needs to sell to keep the shareholders happy and the servers running, their users need to be kept on the site. And this is where engaging, sticky content comes in. Despite the Internet’s love of a false dichotomy, Graph Search does not need to be about beating Google in search. It merely needs to improve Facebook for its users, so they can help Facebook meet its business objectives.

The gap between where you are and where you should be

The gap between where you are and where you should be

3am is not the busiest time of day for search. Nor is it the best for conversions. For organic traffic, this does not matter. Ranks do not change depending on the time of day or week in the search results. The same is not true for paid search.

Search activity and volume change over time, and most of the time, in predictable ways. There are patterns that repeat from day to day, from week to week and from year to year. These changes over time can be important for managing organic search engine marketing. Understanding how demand and interest change and when is important for planning site development and content.

Typically the cycles in demand that are important to search engine optimisation (SEO) are longer than those that matter in paid search (though updates like Caffeine have shortened these). Generating visibility for specific changes in search within the organic results is not as straightforward as a media buy.

Timing and effectiveness are limited by how appropriate the content is for the targeted queries, how successful promotional and linking activity was and how the search engines crawl and rank the content. Paid search does not have the same limitations.

Visibility, Productivity and Competitive Bidding

Adwords Ad Schedule

AdWords Ad Schedule feature

Paid search and display advertising platforms such as AdWords let advertisers manage their campaigns by hour of day and day of the week. How an optimised campaign will use these tools will depend on industry trends, how and when their target market uses search and their own objectives. Unfortunately there is usually more than one advertiser doing this.

Choosing when to push for more traffic and impression share on AdWords and other realtime bidding-based platforms is important. Cost per acquisition (CPA), click through rate (CTR) and conversion rate (CVR) are all good indicators for what is performing and what isn’t.

Another factor to take into consideration is competition. If a day or time works for you, it is likely that it would work just as well for your competitors, and be just as desirable to them as a result. Consequently, when a particular time or day stops performing as well as expected, it could be due to increased competetion rather than just the market. AdWords provides a number of metrics that make it possible to analyse for competitive pressure:

  • Search impression share
  • Average position
  • Average cost per click (CPC)
  • CTR

Search impression share is the most straightforward of these: it does what it says. The information is available down to ad group level, and assuming these are tightly themed, it will give a good indication of what kind of share of voice you have within those query groups.

The other metrics do not directly measure competition, but they can show its effects. Average position is not a reliable metric. It represents an average of all the positions the ads have appeared in for the period, but gives no indication of spread. However, it can indicate large general movement. Changes in CPC and CTR are more reliable indications of competitive activity. Changes in CPC can signal changes in bidding and CTR can also indicate changes in position. Together these two metrics can indicate changes in competitor activity, barring other confounding factors.

The Why of Benchmarking

Monitoring changes within the account can only provide insights when compared to something. Benchmarking makes the difference between identifying a shift in the market and noise. Choosing what to benchmark campaign changes against will depend on what other information is available. Other campaigns that experience the same user behaviour and market similar products are one possibility; organic search traffic for the same kinds of terms is another covering a similar time period.

While there are a few ways to approach analysing and processing this information to create actionable insights, setting some kind of a benchmark matters. Because of AdWords’ nature, many of the changes in cost and behaviour are as likely to be caused by your competition as by changes made to the campaigns and changes in actual user behaviour. Consequently, it is important to be able to differentiate between each.

Finding dates on Google

Finding dates on Google

Event dates such as the one above seem to be another example of Google’s drive to providing a richer, more informative Search Engine Result Page (SERP). It certainly is interesting that this kind of information seems to have only just started to show up. Especially as earlier this week Google announced a great new tool for webmasters, the Data Highligher.

Now available through Google Webmaster Tools (GWT) the Data Highlighter makes it easier to help Google identify structured data on your site. For now the tool is only available for English language sites and for events related information like concerts, festivals, sporting events and festivals.

The Data Highlighter as it is rolled out will become a popular alternative to Schema.org. It will make some forms of structured data easier to implement while requiring fewer resources and it is included with one of Google’s own widely used tools.

Structured Data’s Slow Burn

Movie Times on Google

Movie Times on Google

Structured date in search isn’t new. Microformats have been around for a while and used by search engines to provide a richer search experience, Schema.org is simply the latest. When Schema.org was released it was overshadowed by Google+, which was announced at the same time.

Unsurprisingly as Google has increased the amount of information it is displaying directly in the search results, structured data has been attracting more and more attention. Google’s release of the Knowledge Graph, the inclusion of structured data preview tools and Bing’s own snapshot feature has certainly indicated an accelerating shift in how search engines serve their customers.

Decision Engines and Portals

The Hobbit Movie

The Hobbit Movie

Search is currently far more than just a list of links as Bing, Google and newer products such as Siri and Google Now are getting better able to answer a user’s question directly and without sending them to a different site or application. With the amount of information now available on the SERPs and the tools such as calculator’s and converters usable from the search bar, it is almost as if Google is becoming a portal.

For many stakeholders in search this is a good thing. Users get the information that they want easier and faster by avoiding poorly designed sites and obtuse navigation. Providing a better experience for searchers allows Google to maintain it’s position in the market, and by doing this means the search engine can provide their advertisers a large potential audience.

And view through conversions are gold

And view through conversions are gold

Dodgy math and a lot of numbers are a bad mix and paid search provides enough of the former to create ample opportunity for the latter. Like many other forms of online advertising, paid search generates masses of data and often leads some apparent professionals to confuse information with insights. Like many other forms of online advertising, data is easily available through platforms like Adwords which it leads some apparent professionals to confuse information with insights.

Recommendations for account expansion based on historic placement or search query reports filtered for cost per acquisition (CPA) or conversion rate (CVR) isn’t as useful as it may seem. In practice these lists are mostly populated by junk. Placements and queries that just happened to get their one conversion a year within the reporting period on so little traffic it manages to match the criteria used. This isn’t data, and it certainly isn’t an insight. But it can cost you money.

Math to the Rescue!

Fortunately there are simple, robust tools you can used to deal with this. Tools able to find what is likely to work and identify the riskier options. It’s called math. Numeracy and a familiarity with statistics and probability can be useful in marketing. In the scenario outlined above, the biggest issue is determining how close the data collected reflects reality.

Discovering how frequently a conversion occured is easy. Strictly speaking, a conversion rate is a measure of what has happened. Just because Keyword F got a conversion rate of 50% from the sample below does not mean it would do this again over 10 or 100 clicks. This holds true for any of the examples below. While this can be an indication of future performance, assumuing a large enough sample and that nothing changes, as the number of clicks go down, so does the reliability of this number.

Keyword Traffic Successes Success Rate
A 100 10 10%
B 80 5 6.25%
C 60 10 16.67%
D 40 15 3.75%
E 20 5 25%
F 10 5 50%

How Wrong are You?

While this data won’t necessarily tell you what to expect for the next 10, 20 or 100 clicks, you can determine within what range the actual conversion rate would be. Confidence Intervals can be used to determine the range within which the actual success rate is likely to be 95% (or 50%, or 99%, or whatever) of the time.

Keyword Traffic Successes Success Rate Interval
A 100 10 10% 4.12% – 15.88%
B 80 5 6.25% 0.95% – 11.55%
C 60 10 16.67% 7.23% – 26.1%
D 40 15 37.5% 22.5% – 52.5%
E 20 5 25% 6.02% – 43.98%
F 10 5 50% 19.01% – 80.99%

Calculating these ranges was done using Wolfram Alpha’s Confidence interval for binomial tool. Unsurprisingly, the smaller the sample used, the greater the range.

Use Numbers Better

There is no excuse for getting this wrong. Not with the easy availability of these kinds of tools and the information to use them. Probability and Statistics is very much relevant to analysing and managing paid search campaigns, much in the same way that Economics can be useful too. Be it constructing a formula in a spreadsheet or using an online calculator, looking a little bit harder at the data you seek to base decisions on isn’t all that hard.